Intelligence-based Reinforcement Learning for Continuous Dynamic Resource Allocation in Vehicular Networks

The rapid advancement of intelligent transportation systems necessitates efficient resource allocation for low-latency and high-bandwidth vehicular services. While traditional reinforcement learning has been widely utilized for resource allocation, it suffers from limitations such as poor generalization and interpretability. To overcome these challenges, we propose a novel Intelligence-based Reinforcement Learning (IRL) al-gorithm, which uses active inference to infer the real world and maintain an internal model of the world by minimizing free energy. We address the inefficiency of active inference by incorporating prior knowledge as macro guidance, ensuring more accurate and efficient training. By constructing the intelligence-based model, we eliminate the need for designing reward functions, which aligns better with human thinking and provides a method to reflect the learning, information transmission, and intelligence accumulation processes. Considering the dynamic and uncertain nature of vehicular scenarios, we apply the IRL algorithm to continuously evolving environments where environmental parameters are not fixed. Extensive simulations confirm the effectiveness of IRL, significantly enhancing the generalization and interpretability of intelligent models.